System for the determination of retroreflectivity of road signs and other reflective objects

ABSTRACT

A system for the determination of retroreflectivity values for reflective surfaces disposed along a roadway repeatedly illuminates an area along the roadway that includes at least one reflective surface using a light source. Multiple light intensity values are measured over a field of view which includes at least a portion of the area illuminated by the light source. A computer processing system is used to identifying a portion of the light intensity values associated with a reflective surface and analyze the portion of the light intensity values to determine at least one retroreflectivity value for that reflective surface.

RELATED APPLICATIONS

The present application is a continuation of U.S. patent applicationSer. No. 13/205,337, filed Aug. 8, 2011, which is a divisional of U.S.patent application Ser. No. 12/419,843, filed Apr. 7, 2009, now U.S.Pat. No. 7,995,796, issued Aug. 9, 2011, which is a continuation of U.S.patent application Ser. No. 11/056,926, filed Feb. 11, 2005, now U.S.Pat. No. 7,515,736, issued Apr. 7, 2009, which is a continuation of U.S.patent application Ser. No. 09/928,218, filed Aug. 10, 2001, now U.S.Pat. No. 6,891,960, issued May 10, 2005, which claims the benefit ofU.S. Provisional Application No. 60/224,761, filed Aug. 12, 2000, and ofU.S. Provisional Application No. 60/296,596, filed Jun. 7, 2001. Thepresent application is also related to U.S. Pat. No. 6,674,878, issuedJan. 6, 2004, and U.S. Pat. No. 6,453,056, issued Sep. 17, 2002, whichis a continuation of U.S. Pat. No. 6,266,442, issued Jul. 24, 2001, eachof which are hereby fully incorporated here by reference.

FIELD OF THE INVENTION

The present invention relates generally to the field of automated objectrecognition. More specifically, the present invention relates to asystem for classifying different types of sheeting materials of roadsigns depicted in a videostream.

BACKGROUND OF THE INVENTION

The goal of using an automated image identification system to recognizeroad signs and traffic signs is well known. Various techniques have beenproposed for the recognition of road signs as part of a real-timeautomated vehicle navigation system. Due to the processing limitationsimposed by a real-time environment, almost all of these techniques haveinvolved template matching of shape and color. Given the wide variationsin lighting and conditions, few if any of these systems provide accurateresults.

Another use of automated road sign recognition is for the purpose ofidentifying and creating an accurate inventory of all road signs andtraffic signs along a given street or highway. In one system asdescribed in U.S. Pat. No. 6,266,442, entitled, issued Jul. 24, 2001, toLaumeyer et al., an acquisition vehicle equipped with video cameras andposition identifying technology, such as global positioning satellite(GPS) receivers, is systematically driven over the roads and streets ina given area to produce a videostream tagged with location information.The tagged videostream is analyzed by computer software routines toperform object recognition of the desired objects, the road signs inthis case. The results of this analysis are exported to an assetmanagement database that stores attributes of the road signs.

Road signs are manufactured from a sheeting material made up of multiplelayered films (one or more colored layers that are fused with a layerthat produces the reflectivity) that is adhered to the sign face. Thereare different types of sheeting material utilized in the road signindustry. Currently, specific attributes about each road sign likeretroreflectivity (measured in candelas/lux/sq. meter) and sheeting typemust be gathered manually by sending personnel in the field to measureretroreflectivity with a handheld device (like the Impulse RMretro-reflectometer from Laser Technology, Inc.) and to visuallydetermine the sheeting type of each sign. Measurements ofretroreflectivity and identification of sheeting type are helpful inevaluating the visibility of a sign and whether it has deteriorated dueto a breakdown in the pigments or reflective material in the sheetingmaterial of the sign. The retroreflectivity and sheeting type can alsobe used to produce a predictive model of how the sign will perform intothe future based on the as-measured characteristics.

Generally, highway and street maintenance departments do notsystematically evaluate the deterioration of the reflective materialsused on road signs and markers. If inspections of road signs or markersare performed, they are typically accomplished by having inspectorsmanually position a handheld retroreflectometer directly on the surfaceof a sign in order to determine a retroreflectivity value for that sign.When there are a large number of road signs or markers (sometimesreferred to as traffic control devices or TCDs) in a given jurisdiction,the task of manually inspecting all of these road signs and markers canbe time consuming and expensive.

One technique for determining retroreflectivity, designated as “R_(A)”generally (and from time to time in this disclosure), which does notrequire that a retroreflectometer be placed directly on a sign isdescribed in U.S. Pat. No. 6,212,480 entitled, issued Apr. 3, 2001, toDunne. The Dunne patent relates to a device commercialized by theassignee thereof and marketed as the “Impulse RM” retro-reflectometer byLaser Technology, Inc., of Englewood, Colo., USA. In use, handhelddevices fabricated according to the Dunne patent are manually directedtoward, or precisely at, a target object and then manually “fired.” Oncefired, the handheld device bounces a laser off the target object andmeasures the reflected laser energy that is then used to determine aretroreflectivity.

There are several drawbacks of the handheld laser arrangement describedby the Dunne patent. The handheld device can only measure a single colorat a time and can only measure one object at a time. The determinationof retroreflectivity for a given object is valid only for the actuallocation, or discrete measurement point, along the roadway at which themeasurement was made by the human operator. In order to validate ameasurement made by such devices, the device must be taken back to theprecise location in the field where an original measurement occurred fora valid comparison measurement to be made.

Another technique established for determining the retroreflectivity ofsigns has been introduced by the Federal Highway Administration (FHWA).The Sign Management and Retroreflectivity Tracking System (SMARTS) is avehicle that contains one high intensity flash source (similar to theHoneywell StrobeGuard™ SG-60 device), one color camera, two black andwhite cameras, and a range-sensing device. The SMARTS vehicle requirestwo people for proper operation—one driver and one system operator topoint the device at the target sign and arm the system. The SMARTStravels down the road, and the system operator “locks on” to a sign upahead by rotating the camera and light assembly to point at the sign. Ata distance of 60 meters, the system triggers the flash source toilluminate the sign surface, an image of which is captured by one of theblack and white cameras. A histogram is produced of the sign's legendand background that is then used to calculate retroreflectivity. A GPSsystem stores the location of the vehicle along with the calculatedretroreflectivity in a computer database.

Like the handheld laser device of the Dunne patent, the SMARTS devicecan only determine retroreflectivity for one sign at a time and can onlydetermine retroreflectivity for the discrete point on the roadway 60meters from the sign. Two people are required to operate the vehicle andmeasurement system. The SMARTS vehicle cannot make retroreflectivitydeterminations for signs on both sides of the roadway in a single passover the roadway, and does not produce nighttime sign visibilityinformation for lanes on the roadway not traveled by the vehicle.Because the system operator in the SMARTS vehicle must locate and tracksigns to be measured while the vehicle is in motion, a high level ofoperational skill is required and the likelihood that a sign will bemissed is significant. Most importantly for purposes of the presentinvention, the SMARTS device makes no attempt to automatically determinesheeting type of a sign.

There are an estimated 58 million individual TCDs that must be monitoredand maintained in the United States and new TCD installations increasethis number daily. For the reasons that have been described, theexisting techniques for determining retroreflectivity do not lendthemselves to increasing processing throughput so as to more easilymanage the monitoring and maintenance of these TCDs. So called automateddata collection systems often require that normal traffic be stoppedduring data collection because either the acquisition vehicle moved veryslowly or because the acquisition vehicle had to come to a full stopbefore recording data about the roadside scene. Furthermore, a humanoperator is required to point one or more measurement devices at a signof interest, perform data collection for that particular sign and thenset up the device for another particular sign of interest. With such alarge number of TCDs that must be monitored, it would be desirable toprovide an automated system for determining the retroreflectivity ofroad signs and markers that addresses these and other shortcomings ofthe existing techniques to enable a higher processing throughput of anautomated determination of the retroreflectivity and sheetingclassification of road signs and markers.

SUMMARY OF THE INVENTION

The present invention is a system for classifying different types ofsheeting materials of road signs depicted in a videostream. Once a roadsign has been identified in the videostream, the frames associated withthat road sign are analyzed to determine each of a plurality of colorspresent on the road sign. An estimated retroreflectivity for each of theplurality of colors present on the road sign is then determined. Bycomparing the estimated retroreflectivity for each of the plurality ofcolors against known minimum retroreflectivity values for thecorresponding color for different types of sheeting materials, anaccurate determination of the classification of the sheeting material ofthe road sign is established. Preferably, certain conditions of grossfailure of the sheeting material are filtered out before classificationof the sheeting material is determined.

In a preferred embodiment, a system for the automated determination ofretroreflectivity values for reflective surfaces disposed along aroadway is utilized to establish the retroreflectivity values for bothforeground and background colors of a road sign for the purpose ofclassifying the sheeting material of that sign. An area along theroadway that includes at least one reflective surface is repeatedlyilluminated by a light source and multiple light intensity values aremeasured over a field of view which includes at least a portion of thearea illuminated by the light source. A computer processing system isused to identify a portion of the light intensity values associated witha reflective surface and analyze the portion of the light intensityvalues to determine at least one retroreflectivity value for thatreflective surface. Color images of the area and locational informationare also generated by the system and are used together with acharacterization profile of the light source to enhance the accuracy ofthe determination of retroreflectivity values.

In contrast to the existing techniques for determining retroreflectivitythat require an operator to target individual signs from a knowndistance, a preferred embodiment of the present invention can determineretroreflectivity without targeting individual signs and canautomatically determine sheeting classification as a result. To overcomethe limitations imposed by the existing techniques, the preferredembodiment employs several enhancements that are designed to improve theaccuracy of evaluating intensity measurements made over a view where thereflective surfaces are not individually targeted and therefore neitherthe distance to the reflective surface or the normal vector to thereflective surface are known.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a capture vehicle used in a preferred embodiment of thepresent invention.

FIG. 2 is a data flow diagram of a preferred embodiment of the presentinvention.

FIG. 3 depicts a block diagram of the systems, subsystems and processesfor capturing and processing roadside information from a moving platformin order to compute retroreflectivity according to the teaching of thepresent invention, wherein the arrows connecting the blocks of thediagram illustrate the connections between the systems and sub-systemsfor computing R_(A) for each reflective asset recorded by the system ofthe present invention.

FIG. 4 depicts in diagram form, a preferred configuration of a sensorsuite for use with a four-wheeled vehicle and the interconnections andcouplings between the physical subcomponents of a system designedaccording to the present invention.

FIG. 5 is a plan view of a divided multi-lane vehicle pathway anddepicts how periodic light intensity measurements may be made as avehicle traverses the vehicle pathway over time and the discretelocations where such periodic light intensity measurements are performedby a data acquisition vehicle operating in accordance with the presentinvention.

FIG. 6 depicts four digital frames of data as captured by the intensitysensor at various discrete locations along the vehicle pathway depictedin FIG. 5.

FIG. 7 depicts a flowchart showing the steps required to convertintensity measurements into foreground and background retroreflectivityfor a single reflective asset.

FIG. 8 depicts a typical light source intensity profile over the visibleelectromagnetic spectrum, which illustrates how different wavelengths ofelectromagnetic radiation possess different light intensities.

FIG. 9 depicts a preferred methodology for creating a retroreflectivityprofile for all lanes and locations adjacent a vehicle pathway for asingle reflective asset or sign which retroreflectivity profile is basedupon a single pass of a data acquisition vehicle over the vehiclepathway.

FIG. 10 illustrates the facts that the normal vector of a reflectiveasset and the sheeting type of such a reflective asset create symmetrythat may be used to determine retroreflectivity values along all rays(or vectors) that have the same relative angle to the normal vector ofthe reflective asset.

FIG. 11 depicts the concept of observation angle (i.e., angle betweenincident light from a light source and an human observer (or lightsensor), of the light as reflected from the face of a reflective asset)in the context of a conventional passenger vehicle traversing a vehiclepathway and where light from the vehicle reflects from a stop sign tothe vehicle operator (shown in ghost).

FIG. 12 depicts the concept of entrance angle (i.e., angle betweenincident light from a light source mounted to a vehicle and a normalvector relative to the substantially flat face of a reflective surfacedisposed adjacent a vehicle pathway).

FIG. 13 is a graphic representation of the retroreflectivity performancefor white sheeting for the three main types of sign sheeting.

FIGS. 14A, 14A-1, 14A-2 and 14B, 14B-1, 14B-2 depict a flowchart for apreferred embodiment of the threshold algorithm used to determine signsheeting type.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Referring to FIG. 1, an acquisition vehicle 10 is equipped with multiplevideo cameras 12 that generate a series of raw videostreams 14representative of a road or street over which the acquisition vehicle 10is traveling. A global positioning satellite (GPS) receiver 16 supplieslocation information that is combined with the raw videostream 14 by aprocessor 18 to generate a tagged videostream 20.

In one embodiment as shown in FIG. 2, the tagged videostream 20 isanalyzed by a computer processor 22 to identify each road sign 30 andgenerate an asset management database 24 containing attribute andlocation information associated with each road sign 30. In an alternateembodiment, the identification of road signs 30 and generation of assetmanagement database 24 is accomplished by the same processor 18 thattags the raw videostream 14. The details of this process are set out inU.S. Pat. No. 6,453,056, issued Sep. 17, 2002, to Laumeyer et al., whichis hereby incorporated by reference.

Either concurrent with or subsequent to the identification of road signs30 and generation of asset management database 24, the computerprocessor 22 evaluates that portion 32 of each video frame or image thatdepicts a road sign 30 to determine a set of color values 40, 42 foreach of a plurality of colors on the road sign 32. A retroreflectivityvalue is generated for each color portion 34, 36 on each frame of thetagged videostream 20 containing the road sign 30 that represents adifferent color value 40, 42. Preferably, the values forretroreflectivity are determined by measuring the intensity of thesignal for each color portion 34, 36. These values are then analyzedover the number of frames containing each color portion 34, 36 to arriveat the maximum retroreflectivity value 44, 46 that corresponds to thecolor value 40, 42 for each color portion 34, 36 of the road sign 30.These maximum retroreflectivity values 44, 46 will be the referencevalues used in the threshold algorithm from which sign sheetingclassification will be determined.

There are three main types of sheeting recognized in the industry: 1)Type I, commonly called Engineer Grade; 2) Type III, commonly calledHigh Intensity; and 3) Type VII, commonly called Prismatic (Prismatic issometimes divided into two groups—Type VIIa called Diamond Grade VIP,and Type VIIb called Diamond Grade LDP). In order to remove the manualelement of determining sheeting type and measuring retroreflectivity,the automated system of the present invention must accuratelydistinguish between these sheeting types. To accomplish this, thepresent invention utilizes the fact that all signs use the same sheetingtype for foreground and background colors, that each road sign will haveat least two colors and that the reflectivity for each color for eachtype of sheeting material has a relatively unique minimum initialretroreflectivity value. Most signs also have either White, Yellow orOrange colors as one of the colors on the sign. According to 3M, amanufacturer of reflective sheeting material, each color of the threesheeting types has a minimum initial retroreflectivity value. Thefollowing table lists the minimum values for common colors of each type:

Type I Type III Type VIIa Type VIIb Color Min.R_(A) Min.R_(A) Min.R_(A)Min.R_(A) White 70 250 430 800 Yellow 50 170 350 660 Orange 25 100 200360 Red 14.5 45 110 215 Green 9 45 45 80 Blue 4 20 20 43

This information is stored in a reflectivity/color database 50. Thecomputer processor 22 accesses the database 50 using the maximumreflectivity value 44, 46 that corresponds to the color value 40, 42 foreach color portion 34, 36 to determine the lowest possible sheeting typefor each color. If the sheeting type is classified the same for all ofthe color portions 34, 36 for a given road sign 30, then the sheetingclass 52 is established as that type and this information is preferablystored in the asset management database 24 along with other attributesof the given road sign 30. If there is a discrepancy in theclassification of sheeting material between different colors, asubsequent analysis by the processor 22 using, for example, a neuralnetwork program, to incorporate other determining factors, such as timeof day, shadow, direction that could affect the retroreflectivity of thelighter colors (white, yellow and orange) more than theretroreflectivity of the darker colors (red, green, blue). In general,retroreflectivity values for lighter colors are weighted more heavily inresolving any discrepancies in classification of sheeting material.

The system described herein acquires many data points along the desiredroadway without specifically targeting any objects of interest. For aroadside sign, the specific geometry of the sign (its orientation withrespect to the roadway) is not necessarily known, nor is it required.The retroreflectivity points determined along the roadway are generatedfor the “as placed” road sign. Road signs will display their bestretroreflectivity performance (have the highest retroreflectivityvalues) at or near the normal vector for the sign face. Since thegeometry of the as-measured sign is not known, the system chooses thehighest retroreflectivity value for that sign as the value that will beused in the threshold algorithm for sign sheeting classification.

There are several factors that can cause retroreflectivity readings forsigns to actually be lower than the values for the underlying sheetingtype. For example, a sign that has a layer of dirt on the face willproduce lower retroreflectivity numbers than usual. If these lowernumbers are used in the threshold comparison algorithm, an incorrectsheeting type may result. These systematic errors can be removed byanalyzing the retroreflectivity profile for the sign. Sign sheetingtypes vary by the intensity of light that is reflected, but they alsohave reflective characteristics that give them unique signatures. FIG.13 shows the profiles for Type I, Type III, and Type VII white signsheeting types. Note the unique profiles for the three sheeting types.The preferred embodiment of the present invention, due to its ability toutilize multiple retroreflectivity points, can determine sheeting typeby matching the as-measured profile with a uniformly reduced (where theentire curve is multiplied by a scaling factor that is less than one)characteristic curve that best correlates to the as-measured profile.This correlation step will establish the “best fit” performance curvebased on the shape of the curve, without consideration for the magnitudeof the curve. This “uniform reduction” of the sign sheeting performancecurve allows the proper sheeting type to be determined, thus overcomingthe problem with signs that have some type of surface anomaly. Theperformance curve correlation described above can be used in one of twoways—either as a validation of the proper determination of sheeting typefrom the threshold algorithm or as one of the qualification criteriaprior to performing the threshold algorithm.

The sheeting types are all manufactured with multiple layers. In orderfor the present invention to accurately compute retroreflectivity forpurposes of determining sheeting type of a given road sign, it is alsonecessary for the system to recognize gross sheeting failures likeextreme color fade, de-lamination and vapor fade (darkening of thedaylight appearance of the sheeting due to the corrosion of the metalsign backing). These gross failures will impact R_(A) measurements ofthe sheeting. Preferably, the sheeting determination system describedherein tests for the absence of these gross failures prior to making anyR_(A) measurements as part of the process of categorizing sheeting type.

Retroreflectivity, designated as “R_(A)” generally (and from time totime in this disclosure), varies according to two key parameters,observation angle and entrance angle. Observation angle 100 (See FIG.11) is the angular displacement between a light source 110 and a lightsensor 120, as measured from an object face surface 130. In the case ofa vehicle 140 driven by vehicle operator 145 moving along a highway 150,observation angle 100 is defined by the distance of the vehicle 140 froma sign face surface 130, the placement of the light source (headlights)110 on the vehicle 140, and the position of the light sensor (eyes ofthe vehicle operator) 120 of the vehicle 140.

Entrance angle 160 (See FIG. 12) is defined as the angular displacementof the incident light 170 relative to the normal vector 180 from theobject face surface 130. Entrance angles are impacted by the angularposition 200 of a sign 190 relative to the highway 150, the sign 190lateral distance 210 from the highway 150, and the distance 220 of thevehicle 140 from the sign 190. The inventors hereof are believed to bethe first persons to successfully decrease the complexity and increasethe efficiency of determination of R_(A) in the field.

The method of automated determination of R_(A) (See FIGS. 3 and 4)preferably utilizes a plurality of subsystems located on/in a capturevehicle 225. These subsystems include a light intensity measurementsystem 230, a vehicle positioning system 240, a color image capturesystem 250 and a data recording system 260. The light intensitymeasurement system 230 preferably includes a high output light source270, a light intensity sensor 280 and an intensity measurement systemcontrol 290. A plurality of intensity measurements 300 are generated bythe intensity measurement system 230 in response to the repeatedstrobing of the high output light source 270. The vehicle positioningsystem 240 preferably includes a GPS receiver 310, an inertialnavigation system 320, a distance measuring instrument 330 and a masterclock 340. A position measurement 350 is generated by the vehiclepositioning system 240. The color image capture system 250 preferablyincludes a stereoscopic camera pair 360, iris controls 370 and imagecapture control 380. The image capture system 250 generates a digitalimagery stream 390.

The data required for the automated determination of R_(A) isaccumulated while traversing a highway 150 with the capture vehicle 225(See FIGS. 5 and 6). The capture vehicle 225 is shown on a 4-lanedivided highway 400 with the capture vehicle 225 located in a proximatelane 410 to the stop sign 190. Preferably, a series of reflected lightintensity frames 420 are generated at a constant measurement interval430 as the capture vehicle travels along the highway 150.

Characterization of sign 190 R_(A) preferably utilizes the datarecording system 260 to create a single tagged videostream 440 from thereflected light intensity frames 420, position measurements 350 anddigital imagery 390 for each capture event 430 (See FIGS. 3, 5, 6, 7,and 8). A computer processor 450 identifies an object of interest 460 ina portion of the intensity frame 420 and determines the object ofinterest attributes 465 associated with that object of interest.Preferably, the objects of interest are identified from the digitalimagery stream 390 generated by the color image capture system 250 inthe manner as taught by U.S. Pat. No. 6,266,442. Alternatively, othertechniques known in the art for isolating an object of interest in avideostream can be used. Preferably, the computer processor 450correlates the portion of an image frame of the digital imagery stream290 with a similar portion of the intensity frame 420 containing theobject of interest 460.

For each object of interest 460, a background intensity measurement 470and a foreground intensity measurement 480 is generated. Using anintensity algorithm 490, a light intensity sensor characterization 275and a look-up-table 475, the computer processor 450 determines abackground luminance value 500 and a foreground luminance value 510.Based on the background luminance value 500, the foreground luminancevalue 510, a characterization of light source wavelength 540, thebackground sheeting color 505 and the foreground sheeting color 506 thecomputer processor 450 characterizes a background R_(A) 520 and aforeground R_(A) 530 which are preferably reported separately for thatobject of interest.

The automated determination of multiple R_(A) values for a given objectof interest 460 allows for the extrapolation of R_(A) values at anunmeasured viewing point 550 for an object of interest, such as a sign190 (See FIGS. 9 and 10). In this example, the unmeasured viewing pointresides in a nontraversed lane 560. The computer processor 450 definesan undetermined retroreflectivity ray 570 for unmeasured viewing point550. Using interpolated values, the computer processor 450 determines anR_(A) value for unmeasured viewing point 550 and any point located alongundetermined retroreflectivity ray 570.

Pursuant to the teaching of the present invention, a method andapparatus for determining retroreflectivity of relatively flat surfaceportions of objects disposed adjacent a highway 150 traversed by avehicle 140 are taught, enabled, and depicted. The present invention maybe utilized to detect and determine a retroreflective surface ofinterest disposed in a scene of non-retroreflective surfaces. That is,at least one object face surface 130 which exhibits retroreflectivityover at least a relatively narrow conical volume of magnitude of severaldegrees from a normal vector 180 originating from said object facesurface 130.

In accordance with the present invention, a determination of theretroreflectivity of objects adjacent a highway 150 preferably includesproviding: (i) position measurements 350 of a capture vehicle 225; (ii)precise position of the object of interest 460, or sign 190; (iii)intensity measurements 300 from a high output light source 270 and lightintensity sensor 280 at measurement intervals 430 along said highway150. Thus, a single-pass along the highway 150 by the capture vehicle225 operating the light intensity measurement system 230, vehiclepositioning system 240, image capture system 250 and data recordingsystem 260 taught herein eliminates many shortcomings of the prior artand allows a single vehicle operator to conduct virtually continuousdata measurement and acquisition of objects of interest 460 disposedadjacent a highway 150, at capture events 430 on said highway 150,without disrupting or interrupting other vehicle traffic traversing saidhighway 150.

FIG. 3 shows a block diagram of the on-board systems and the desktopsystems required to record a tagged videostream 440 and create signR_(A) profiles 590 for various signs 190 along a highway 150. Thevehicle positioning system 240 contains all of the equipment toprecisely locate the capture vehicle 225. All location information issynchronized with a master clock 340 preferably associated with acomputer processor 450, which allows other data types to be merged withthe vehicle location information at later stages in the post-processing.All of the on-board systems utilize the same master clock 340information, thus allowing any events (image capture system 250,intensity measurement 300, and trigger controls 227, 228) to becorrelated to the precise vehicle location and attitude duringreal-time, near real-time, or post-processing of the data acquired bythe capture vehicle 225.

The image capture system 250 consists of at least one set ofstereoscopic cameras 360 that gather digital imagery along the highway150. Each capture event is combined with time stamp information from thevehicle positioning system 240 which also provides trigger control 227for the image capture system 250 and trigger control 228 for the lightintensity measurement system 230. These images and associated timestamps are later combined with photogrammetry to create objects ofinterest 460 and their associated attributes 465.

The light intensity measurement system 230 preferably consists of atleast one high output light source(s) 270 and the associated lightintensity sensor(s) 280. The precise control for these items iscontained within the light intensity measurement system 230, and mastertime sequencing instrument 340 information received from the vehiclepositioning system 240 (or computer processor 450) is combined to createa tagged videostream 440 so precise vehicle information can be utilizedduring post-processing.

The data recording system 260 is constantly monitoring controlinformation from the other three on-board systems and records thenecessary information. No post-processing is performed in the datarecording system 260. As computer power increases in the future, oneskilled in the art could produce a system whereby most, if not all, ofthe post-processing functions were performed in the capture vehicle 225,perhaps even in real time. The inventors can imagine several uses forthe production of real-time information from the image capture system250 in the future, but the cost of obtaining such information withtoday's computing power makes this option prohibitively expensive today.

The lower half of FIG. 3 shows the functional blocks for datapost-processing. There are two main functions—the creation of objects ofinterest 460 and their associated attributes 465, and the determinationof retroreflectivity for each object of interest 460. There are manymethods for creating objects of interest 460 from digital imagery, a fewof which are discussed in this disclosure. The specific steps requiredto compute R_(A) are outlined in the discussion below.

FIG. 4 shows a typical configuration within a capture vehicle that iscapable of producing data and imagery to create digital representationsof objects of interest 460 and objects of interest retroreflectivity466. The distance measuring instrument (DMI) 330, GPS receiver 310 andinertial navigation system (INS) 320 constitute the vehicle positioningsystem 240. Not all of these components are necessary to obtain thedesired results, but better precision, and therefore more meaningfuldata, are produced if all three components are included.

The high output light source(s) 270 and light intensity sensor(s) 280constitute the light intensity measurement system 230. These componentsmake it possible to gather on-the-fly information for a desired highway150 to allow the computation of object of interest retroreflectivity466, as well as create a full 3-D sign R_(A) profile 590 for those sameobjects of interest 460.

The stereoscopic cameras 360 constitute the digital imagery system 390that allows for the creation of objects of interest 460 and theirassociated attributes 465 during post-processing. More than one set ofstereoscopic cameras 360 can be employed, thus increasing the accuracyof positional measurements for objects of interest 460. Other,non-stereoscopic imaging systems could also be employed with little orno change to the vehicle positioning system 240 or to the lightintensity measurement system 230.

FIG. 5 shows the top view of a 4-lane divided highway 400 with a stopsign 190. The capture vehicle 225 is traveling in the proximate lane 410to the stop sign 190 and makes intensity measurements 300 at captureevents 430 while traveling the depicted route. The techniques describedherein will allow a retroreflectivity value for this stop sign 190 to becomputed for any point along the 4-lane divided highway 400, independentof whether the intensity measurement 300 was made at that point and alsoindependent of whether the capture vehicle 225 actually drove over thatpoint.

It should be noted that intensity measurements 300 are made continuouslywhile the capture vehicle 225 is in motion, thus requiring no priorknowledge of either the positions or the existence of signs.

FIG. 6 shows some typical reflected light intensity frames 420 ascaptured by the light intensity sensor 280 at various discrete locationsalong the roadway. These reflected light intensity frames 420 are theresult of the high output light source 270 being energized (or flashed)while each reflected light intensity frame 420 is captured by one ormore light intensity sensors 280. Since most of the objects in the sceneare not reflective, and due to the high setting of the threshold rangein the light intensity sensor(s) 280, the reflected light intensityframes 420 will actually show very few objects. For effective luminanceresults throughout a wide range of retroreflective materials, more thanone light intensity sensor 280 may be required in order to get enoughlevels of gray within the active part of the visible spectrum. Whenmultiple light intensity sensors 280 are required or used, they may eachhave different threshold ranges and each thus detect luminance values indifferent parts of the desired luminance ranges.

In order to compute retroreflectivity (R_(A)), one needs to know theluminance of the reflected energy. Luminance (expressed in candelas persquare meter, or cd/m²) will vary according to the intensity sensorcharacterization profile 275 of the light intensity sensor(s) 280 andthe color of the material from which light is reflected.

Most roadway signs 190 contain text and/or symbols overlaid on abackground. To ensure maximum visibility during day and nightconditions, the colors of the foreground information (text and/orsymbols) are chosen to have maximum day and night contrast with thebackground material. The techniques taught herein allow theretroreflectivity of roadway signs 190 to be determined for bothforeground and background materials. Computing both the foreground 530and background retroreflectivity 520 for each object of interest 460allows us to ensure that the proper nighttime contrast is achieved forroadside assets. For example, a stop sign 190 with a red background andwhite lettering can provide good daytime contrast between the text andthe sign background. But if these two materials display very similarretroreflectivity characteristics, their nighttime contrast will beminimal, thus rendering the sign ineffective during nighttimeconditions.

FIG. 7 shows a block diagram of the steps required to transformintensity measurements 300 into foreground luminance values 510 andbackground luminance values 500. First, a black and white camera ispreferably used as a light intensity sensor 280 to maximize thesensitivity of intensity measurements 300 (intensity will be determinedfrom the gray value of the corresponding pixels). Think of an intensitymeasurement 300 as intensity values for N discrete points within thescene, where N corresponds to the number of pixels in the lightintensity sensor's 280 array. For a light intensity sensor 280 that hasa resolution of 640×480 pixels, there are 307,200 discrete intensityvalues for each intensity sensor measurement 300. Although the preferredembodiment utilizes an intensity sensor measurement 300 in the form ofan array of discrete pixel intensity values, preferably a single pixelintensity value is selected and utilized for the automated determinationof a corresponding retroreflectivity value. Alternatively, an average orother combination of a group of pixel intensity values could be utilizedfor the automated determination of a corresponding retroreflectivityvalue. Intensity values will vary according to the color of thereflected light, since not all colors of incoming light excite the lightintensity sensor 280 pixels in the same way. By knowing the backgroundor foreground color of the object of interest 460 along with the lightintensity sensor's 280 ability to sense, or the light intensity sensor's280 profile for a particular color, the intensity value 300 for aparticular color can be converted into a luminance value. Lightintensity sensor 280 characterization is essential for high precisioncomputations since N photons of a given particular color (or wavelength)of light will represent a different gray value (intensity level) in thesensor than N photons of another color (or wavelength) of light. Thelook-up-table (LUT) 475 shown in FIG. 7 is a digital table stored inmemory that uses the indexes of intensity (a single gray level valuefrom the intensity measurement 300) and sheeting color to determine theluminance. The light intensity sensor characterization 275 is empiricalinformation about the light intensity sensor 280 that is used to createthe LUT 475. The same LUT 475 is used for computing foreground 510 andbackground luminance values 500.

The reader should note and appreciate that luminance is strictly ameasure of the reflected light, while retroreflectivity (or R_(A),expressed in candelas/lux/m²) is a measure of the reflected light withrespect to the incident light for that object. FIG. 7 shows theinformation needed to accurately convert luminance to R_(A): sensorlocation, object location, light source characterization, and color ofreflective material. For less precise R_(A) computations, a subset ofthe aforementioned characteristics can be utilized. For example, if auniform light source (equal intensity throughout the scene), columnatedlight reflected from the surface of the object of interest 460, and aknown distance 220 between the object of interest 460 and the lightintensity sensor 280 are all assumed, then the sheeting color andluminance value may be used to determine a rough approximation (within20%, for example) for R_(A).

To obtain the highest quality R_(A) calculations, all of the data shownin FIG. 7 should be utilized. The characterization of light source angledefines the amount of light emitted from the high output light source270 throughout the source's field of view. Due to the limitations oflamp design and their associated reflectors, most semi-uniform lightsources will have their greatest intensity at or near the normal vectorfor the light source. Since the high output light source(s) 270 are notaimed at objects of interest 460, the part of the incident light beamthat is striking the object of interest 460 when the intensitymeasurement 300 is captured must be determined. Light source anglecharacterization is a process whereby empirical data from the light ismodeled to establish the light intensity for numerous discrete vectorsfrom the center of the light. When intensity values are determined for adiscrete point in the scene (from the object's face surface 130), thelight intensity sensor 280 location and heading, as well as the objectof interest 460 location, are used to determine which light vectoremanating from the light source was responsible for the resultingintensity measurement. The characterization of light source angletherefore, is a look-up-table where an object of interest's 460 angulardisplacement from the normal vector 180 for the high output light source270 is converted to a light intensity for the associated vector.

Since the beam from the high output light source 270 is diverging,objects of interest 460 farther from the origin of the light willreceive less incident radiation than those objects of interest 460closer to the light. The characterization of light source angle isconstructed at a few discrete distances from the light. Simple geometrycan be used to compute the incident radiation (using an interpolationmethod for an actual distance between two discrete distances in thecharacterization of light source angle) hitting the actual object ofinterest 460 based on the empirical data from the characterization oflight source angle.

The preferred high output light source 270 is a uniform full-spectrum(visible spectrum) light. In practice, this light source will not emitthe same intensity for all wavelengths of visible light. One variable oflight source color characterization that should be considered is theoutput profile of the light throughout the visible spectrum. FIG. 8shows a typical full-spectrum light source output profile. Note that theintensity in the blue area (400-500 nm) of the spectrum is stronger thanin the red area (600-700 nm). This profile specifies the amount of lightenergy (number of photons) emitted for a given frequency. Since R_(A)depends on the intensity of the incident light, the light source colorcharacterization 540, light source angle characterization 535,background sheeting color 505 and foreground sheeting color 506 must becombined to determine how the background luminance value 500 andforeground luminance value 510 is converted to R_(A) (i.e., what percentof the incident photons of the foreground/background color werereflected back to the sensor).

The divergence pattern for the light source may have different profilesfor various portions of the visible spectrum. In practice, a separatelight source angle characterization profile may be required for eachpossible foreground and background color of any given object of interest460.

A preferred high output light source 270 is of the type set forth in theattached installation and operation guide entitled “StrobeGuard™ HighIntensity Obstruction Lighting System, Model No. SG-60,” manufactured byHoneywell, Inc. In order to create a useful sign R_(A) profile 590 foran object of interest 460, intensity measurements 300 for frequentcapture events 430 along a highway 150 while the capture vehicle 225 isin motion. For example, at vehicle speeds of 50 miles per hour,intensity measurements 300 should be taken at a rate of at least two persecond. The StrobeGuard™ SG-60 model has a recharge time of about 1.5seconds between successive flash events. As a result, one SG-60 lightwill not provide enough flash events per second to allow an adequatenumber of intensity measurements 300. In order to meet the requirementsof two flash events per second for a capture vehicle 225 traveling at 50miles per hour, three of the StrobeGuard™ SG-60 units would need to befired in a synchronized, round-robin pattern to obtain enough triggerevents.

The light intensity measurement system 230 described herein attempts toremove observation angle 100 as an R_(A) variable. This is done bykeeping the offset between the high output light source(s) 270 and lightintensity sensor(s) 280 as low as possible. As mentioned previously, anR_(A) profile of a simulated roadway 580 can be computed, even thoughthe intensity was not measured at every point and even though thecapture vehicle 225 did not drive over every point. First, it iscritical that the geometry of R_(A) is understood. Reflective materialslike sign sheeting are designed to project near-columnated light backtoward the light source. If a perfectly columnated light being reflectedfrom the object of interest 460 being measured and a zero observationangle are assumed, the R_(A) values for all discrete locations along aray projected from the object will be identical.

FIG. 9 shows how to compute R_(A) for any discrete location along a4-lane divided highway 400. The R_(A) value for the desired point willbe based on the R_(A) value that lies along the pathway traveled by thedata acquisition vehicle 225. To compute this “reference R_(A) value”(the R_(A) value for a discrete location on or along a vehicle path), anundetermined retroreflectivity ray 570 is drawn from the desiredlocation to the face of the reflective asset. The discrete locationwhere the undetermined retroreflectivity ray 570 intersects the vehiclepath will be used as the reference R_(A) value. Since the discretelocation on the vehicle path will always lie between two measuredlocations where intensity measurements 300 were made, the referenceR_(A) value is computed by interpolating the two closest (in distance)R_(A) values along the vehicle path. As used herein, interpolate has theusual and typical meaning. It will be understood that interpolationconsistent with the present invention can involve interpolation followedby extrapolation and shall also include such other special mathematicalexpressions used or created to account for border effects and effects atthe lateral periphery and at the furthest distance where R_(A) may bereliably determined by application of the teaching of this disclosure.

If perfectly columnated light is assumed, the value of R_(A) at thedesired point will be the same as the reference R_(A) value. Inpractice, all sign 190 sheeting materials will have some beam divergencefor reflected light. This beam divergence information can be used toadjust the computed R_(A) value up (or down) from the reference R_(A)value for discrete locations closer to (or farther from) the object'sface surface 130.

While knowing the normal vector 180 to a sign 190 face is not required,there are some advantages for planning and maintenance purposes thatmake the information useful. Several ways to compute the normal vector180 for a sign 190 exist. First of all, the “assumption” method requiresthat the normal vector 180 from the surface of the sign 190 is assumedto be parallel to the capture vehicle pathway 410 at the nearestlocation of the capture vehicle pathway 410 to the sign 190. Second, ascanning laser operating in conjunction with an optical sensor andhaving a common field of view may be used to more precisely resolve thenormal vector 180 from the object's face surface 130. Third,stereoscopic cameras 360 may be employed in a useful, albeit veryimprecise manner of determining the normal vector 180. Fourth, theassumption method and stereo imaging method may be combined whereby thenormal vector 180 is assumed to lie parallel to the vehicle pathway 410unless the stereo imaging output renders the assumption false.

Of the methods listed above, the highest precision measuring systems fordetermining the normal vector 180 consists of a scanned laser andassociated optical sensor. This combination yields relative distancemeasurements between the capture vehicle 225 and the object's facesurface 130 that are more precise than optical measurements withcameras. A laser scanner attached to the capture vehicle 225 anddirected toward a roadside scene populated with retroreflective signs130 generates a series of reflection points to the optical sensor thatappear as a horizontal segment of points. The optical sensor must befast enough (i.e., have adequate data acquisition rates) to capture atleast several individual discrete measurements across the object's facesurface 130 (or of any other reflective asset). In general, two types oflaser scanners are suitable to be utilized according to the presentinvention; namely, single-axis scanners and dual-axis scanners. Apreferred sensor is of the type set forth in the proposal entitled,“Holometrics 3-D Vision Technology,” as referenced in the previouslyidentified provisional patent application.

Since most all types of roadside signs 190 to be measured are disposedat various elevations relative to the highway 150 and the capturevehicle 225, a single-axis laser scanner cannot be mounted such that thescanning laser beam covers only a single elevation or constant heightrelative to the highway 150 and the capture vehicle 225. Rather, theinventors hereof suggest that use of a single-axis type laser scannermust either be mounted high on the capture vehicle 225 with a downwardfacing trajectory, or be mounted low on the capture vehicle 225 with anupward facing scanning trajectory. These two mounting schemes for asingle-axis laser scanner help ensure the lateral scan will intersectwith virtually every object face surface 130 of all signs 190 or otherobjects of interest 460 present in a roadside scene regardless of theelevation or height or such signs relative to the roadway or to themoving platform.

Dual-axis laser scanners 335 circumvent the varying sign height probleminherently encountered if a single-axis laser scanner is employed as thesource of integrated energy when practicing the teaching of the presentinvention. A dual-axis laser scanner 335 operates by continuously movingthe scanning beam scan up and down at a relatively slow rate whilesweeping the laser beam laterally from side to side across the field ofview at a relatively more rapid rate.

In order to obtain the normal vector 180 for a sign 190 as taughthereunder, only a select horizontal series of discrete locations acrossthe object's face surface 130 needs to be sensed by the high-speedoptical sensor. For each point in the horizontal series of discretelocations recorded for a given sign 190 due to the incident radiationprovided by the scanning laser, as sensed by the high speed opticalsensor, the precise direction of the incident laser is recorded, thusallowing both distance and direction of the measured point to bedetermined.

Either of the scanning methods produces a massive number of senseddiscrete locations representing discrete reflections of the incidentlaser radiation and each must be processed in order to correlate each ofthe sensed discrete locations with the object's face surface 130. Oncethe lateral series of discrete locations for a sign 190 is determined,simple triangulation methods are used to combine: (i) the vehiclelocation, (ii) vehicle heading vector, and (iii) scanned sign point toultimately determine the normal vector 180 for the object's face surface130.

As stated earlier, knowing the sign's 190 normal vector 180 can expandthe utilization of the present invention. The retroreflective propertiesof sign 190 sheeting materials are typically symmetrical about thevertical axis of the object's face surface 130. Because of thissymmetry, R_(A) values (either computed or extrapolated/interpolatedvalues) will be identical for rays that are symmetrical about thevertical axis.

FIG. 10 shows how the sign's 190 normal vector 180 can be used toextrapolate more R_(A) points. The R_(A) value for Point B is the sameas the R_(A) value for Point A since their angle relative to the normalvector 180 is the same and since their distance from the sign 190 is thesame. If Point B has the same relative angle (from the sign's 190 normalvector 180) as Point A, but lies closer to (or farther from) theobject's face surface 130, the sign 190 material's beam divergenceprofile can be used to adjust the R_(A) value for Point B up (or down)from the value obtained for Point A.

The image capture system 250 and light intensity measurement system 230are preferably free running, with measurements being made periodicallyduring capture vehicle 225 operation. There is no requirement that thesetwo systems be synchronized. In fact, these systems could operate incompletely different capture vehicles 225, if necessary. When bothsystems are contained within the same capture vehicle 225, the onlyconstraint for simultaneous operation is placed on the image capturesystem 250. Because of the intensity of the high output light source 270in the light intensity measurement system 230, it is preferred that theimage capture system 250 not capture frames at the same instant that thehigh output light source 270 is triggered. If images are actuallycaptured while the high output light source 270 is triggered, theirpositional results would still be valid, but the colors displayed wouldbe inaccurate because of the high output light being directed toward the(typically lower-thresholded) stereoscopic cameras 360.

One skilled in the art could completely eliminate any need for the imagecapture system 250 to know the firing events of the light intensitymeasurement system 230 by choosing sampling rates for the two systemsthat do not share any harmonic frequencies. On the rare occasions whenthe image capture system 250 captures images while the high output lightsource 270 is energized (or flashed), the skilled implementer could usetime stamps to determine when this system simultaneity occurred anddiscard the imaging frames.

Referring now to FIGS. 14A, 14A-1, 14A-2 and 14B, 14B-1, 14B-2, apreferred embodiment of the flowchart for the sign sheeting thresholdalgorithm will be described. In the preferred embodiment, certainassumptions are made that simplify the threshold algorithm process. Itwill be understood that additional assumptions could be made to furthersimplify the process, or that the process could be further expanded toallow certain of the assumptions to be avoided. In the preferredembodiment, it is assumed that each road sign 30 has only two colors ofreflective sheeting and that the sheeting type is the same for both thebackground sheeting color 505 and the foreground sheeting color 506. Itis also assumed that the retroreflectivity for both the background R_(A)520 and the foreground R_(A) 530 are non-zero values. As previouslydescribed, the algorithm assumes that prefiltering has eliminatedretroreflectivity values for road signs 30 that demonstrate some type ofgross failure of the sheeting material, such as delamination, excessivesurface wear, extreme color fade, vapor fade, graffiti, or excessivedirt or other obstructions that would prevent an accurate determinationof the retroreflectivity value. Such filtering is preferablyaccomplished by image analysis of the color images using any number ofknown image analysis techniques for characterizing anomalies in images.

The sign sheeting threshold algorithm process is initiated at step 600.At steps 610-624, the background sheeting color 505 is compared to aseries of known sign sheeting colors. If the background color isyellow-green as determined at step 610, then the sign sheeting type isclassified as Type VII as step 628. If the background color is white asdetermined at step 612, then the background R_(A) 520 is compared to themaximum retroreflectivity values for different sheeting types at steps630, 632. If the background R_(A) 520 is less than the maximum whiteretroreflectivity value for sheeting Type I as determined at step 630,then the sign sheeting type is classified as Type I at step 634.Otherwise, if the background R_(A) 520 is less than the maximum whiteretroreflectivity value for sheeting Type III as determined at step 632,then the sign sheeting type is classified as Type III at step 636. Ifneither steps 630 or 632 are satisfied, then the sign sheeting type isclassified as Type VII at step 638. A similar process is repeated forcolors yellow at step 614 and steps 640, 642, orange at step 616 andsteps 644, 646, red at step 618 and steps 650, 652.

If the background color is either green, blue or brown, as determined atsteps 620, 622 and 624, then a second determination is made at step 660whether the foreground color 506 is white and at step 670 whether theforeground color is yellow. If step 660 is satisfied, then theforeground R_(A) 520 is compared to the maximum retroreflectivity valuesfor different sheeting types at steps 662, 664. If the foreground R_(A)530 is less than the maximum white retroreflectivity value for sheetingType I as determined at step 662, then the sign sheeting type isclassified as Type I at step 666. Otherwise, if the foreground R_(A) 530is less than the maximum white retroreflectivity value for sheeting TypeIII as determined at step 664, then the sign sheeting type is classifiedas Type III at step 668. If neither steps 662 or 664 are satisfied, thenthe sign sheeting type is classified as Type VII at step 669. A similarprocess is repeated for the yellow foreground color at steps 672 and674.

In the event that the background color 505 was not identified in steps610-624 or the foreground color 506 was not identified in steps 660,670, the sign sheeting type is considered undetermined at step 680. Itwill be understood that various options can be undertaken at step 680,including obtaining another color image and set of retroreflectivityvalues for the given road sign either with or without additionalfiltering or preprocessing of the raw data, marking the image and datafor further review by an operator, discarding the information andmarking the road sign as needing manual investigation, marking the roadsign as needing replacement, or any combination of these or otheroperations.

Although the preferred embodiment of the threshold algorithm has beendescribed with respect to the use of maximum retroreflectivity values,it will be understood that the threshold algorithm could utilize eitherminimum or maximum retroreflectivity values. Similarly, the combinationsand orders of comparison of the colors and foreground or backgroundcolors may be altered and additional comparisons may be made toaccommodate additional sheeting types that may be developed. Preferably,the colors 505 and 506 are determined based on CIELUV color values asevaluated by the image capture system 250. Alternatively, otherequivalent color value systems such as RGB could be used for the colorvalues. Preferably, the color values for the comparisons are a range ofvalues specified by the manufacturer of the sheeting material.Alternatively, the color values for the comparison can be ranges ofvalues empirically established.

What is claimed:
 1. A method of automated determination ofretroreflectivity values for reflective surfaces disposed along aroadway comprising: activating a light source on a capture vehicle,while the capture vehicle is moving along the roadway, to illuminate anarea that includes at least one reflective surface, the at least onereflective surface including a foreground surface and a backgroundsurface; determining a plurality of light intensity values with at leastone intensity sensor directed to cover a field of view which includes atleast a portion of the area illuminated by the light source; while thecapture vehicle is moving, capturing a color image with a camera mountedon the capture vehicle and directed to cover a field of view whichincludes at least a portion of the area illuminated by the light source,the camera being distinct from the at least one intensity sensor; andusing a computer processing system to: identify a portion of theplurality of light intensity values, the portion of the plurality oflight intensity values being associated with one of the at least onereflective surfaces; analyze the portion of the plurality of lightintensity values; and determine at least one retroreflectivity value forthe one of the at least one reflective surfaces using the portion of theplurality of light intensity values, the captured color image, and atleast one of: a sheeting color of the background surface of the one ofthe at least one reflective surfaces and a sheeting color of theforeground surface of the one of the at least one reflective surfaces.2. The method of claim 1, wherein determining a plurality of lightintensity values comprises measuring luminance.
 3. The method of claim1, wherein determining a plurality of light intensity values comprisesmeasuring light intensity.
 4. A System for acquiring information toassess reflective surfaces disposed along a roadway comprising; acapture vehicle having:. a light source, on the capture vehicle; a lightsensor; a camera, mounted on the capture vehicle, the camera beingdistinct from the light sensor; and a control system operably connectedto the light source, the camera and the light sensor such that i) thelight sensor records light intensity information associated with an areathat includes at least one reflective surface and ii) the cameracaptures a color image that includes color image information associatedwith an area that includes the at least one reflective surface, whilethe capture vehicle is moving along the roadway, in response to repeatedillumination of the area by the light source while the capture vehicleis moving, the at least one reflective surface including a foregroundsurface and a background surface; and a computer processing system that;identifies a portion of the plurality of light intensity values from therecorded light intensity information, the portion of the plurality oflight intensity values being associated with the at least one reflectivesurface, analyzes the portion of the plurality of light intensityvalues, and determines at least one retroreflectivity value for the atleast one reflective surface using the portion of the plurality of lightintensity values, the captured color image, and at least one of: asheeting color of the background surface of the at least one reflectivesurface and a sheeting color of the foreground surface of the at leastone reflective surface.
 5. The system of claim 4, wherein the lightsensor records luminance.
 6. The system of claim 4, wherein the lightsensor records light intensity.
 7. A method of automated determinationof retroreflectivity values for reflective surfaces comprising:activating a light source on a capture vehicle, while the capturevehicle is moving along a roadway, to illuminate an area that includesat least one reflective surface without targeting the light source onthe at least one reflective surface, the at least one reflective surfaceincluding a foreground surface and a background surface; determining aplurality of light intensity values with at least one intensity sensordirected to cover a field of view which includes at least a portion ofthe area illuminated by the light source; while the capture vehicle ismoving, capturing a color image with a camera mounted on the capturevehicle and directed to cover a field of view which includes at least aportion of the area illuminated by the light source, the camera beingdistinct from the at least one intensity sensor; and using a computerprocessing system to: identify a portion of the plurality of lightintensity values associated with one of the at least one reflectivesurface; analyze the portion of the plurality of light intensity values;and determine at least one retroreflectivity value for the one of the atleast one reflective surface using the portion of the plurality of lightintensity values, the captured color image, and at least one of: asheeting color of the background surface of the at least one reflectivesurface and a sheeting color of the foreground surface of the at leastone reflective surface; wherein the portion of the plurality of lightintensity values comprises a frame of pixel intensity values and aplurality of reflective surfaces are present in the portion of theplurality of light intensity values.
 8. The method of claim 7, whereindetermining a plurality of light intensity values comprises measuringluminance.
 9. The method of claim 7, wherein determining a plurality oflight intensity values comprises measuring light intensity.
 10. A methodof automated determination of retroreflectivity values for reflectivesurfaces comprising: activating a light source on a capture vehicle,while the capture vehicle is moving along a roadway, to illuminate anarea that includes at least one reflective surface without targeting thelight source on the at least one reflective surface, the at least onereflective surface including a foreground surface and a backgroundsurface; determining a plurality of light intensity values with at leastone intensity sensor directed to cover a field of view which includes atleast a portion of the area illuminated by the light source; while thecapture vehicle is moving, capturing a color image with a camera mountedon the capture vehicle and directed to cover a field of view whichincludes at least a portion of the area illuminated by the light source,the camera being distinct from the at least one intensity sensor; andusing a computer processing system to: identify a portion of theplurality of light intensity values associated with one of the at leastone reflective surface; analyze the portion of the plurality of lightintensity values; and determine at least one retroreflectivity value forthe one of the at least one reflective surface using the portion of theplurality of light intensity values, the captured color image, and atleast one of: a sheeting color of the background surface of the at leastone reflective surface and a sheeting color of the foreground surface ofthe at least one reflective surface; wherein the step of activating thelight source is synchronized to the step of determining the plurality oflight intensity values.
 11. The method of claim 10, wherein determininga plurality of light intensity values comprises measuring luminance. 12.The method of claim 10, wherein determining a plurality of lightintensity values comprises measuring light intensity.
 13. A. method ofautomated determination of retroreflectivity values for reflectivesurfaces comprising: creating a characterization profile for a lightsource, the characterization profile including an array of knownluminance values associated with reflections of the light source;creating a characterization profile for an intensity sensor, thecharacterization profile including an array of intensity values measuredfor reflections of a known light source; activating the light source ona capture vehicle, while the capture vehicle is moving along a roadway,to illuminate an area that includes at least one reflective surface, theat least one reflective surface including a foreground surface and abackground surface; determining a plurality of light intensity valueswith at least one intensity sensor without targeting a particularreflective surface; while the capture vehicle is moving, capturing acolor image with a color camera mounted on the capture vehicle anddirected to cover a field of view which includes at least a portion ofthe area illuminated by the light source, the color camera beingdistinct from the at least one intensity sensor: using a computerprocessing system to determine at least one retroreflectivity value forone of the at least one reflective surface, including: identifying aportion of at least one light intensity value associated with one of theat least one reflective surfaces; utilizing the characterization profileof the light source and the characterization profile for the intensitysensor to determine a luminance value associated with the portion ofthat light intensity value associated with that reflective surface basedon the at least one color associated with that reflective surface; andconverting the luminance value to determine a retroreflectivity valuefor the one of the at least one reflective surfaces using the portion ofthe at least one light intensity value, the captured color image, and atleast one of: a sheeting color of the hacks and surface of the one ofthe at least one reflective surfaces and a sheeting color of theforeground surface of the one of the one of the at least one reflectivesurfaces.
 14. The method of claim 13, wherein determining a plurality oflight intensity values comprises measuring luminance.
 15. The method ofclaim 13, wherein determining a plurality of light intensity valuescomprises measuring light intensity.
 16. A method of automateddetermination of retroreflectivity values for reflective surfacescomprising: activating a light source on a capture vehicle, while thecapture vehicle is moving along a roadway, to illuminate an area thatincludes at least one reflective surface without targeting the lightsource on the at least one reflective surface, the at least onereflective surface including a foreground surface and a backgroundsurface; determining a plurality of light intensity values with at leastone intensity sensor directed to cover a field of view which includes atleast a portion of the area illuminated by the light source; while thecapture vehicle is moving, capturing a color image with a camera izmounted on the capture vehicle and directed to cover a field of viewwhich includes at least a portion of the area illuminated by the lightsource, the camera being distinct from the at least one intensitysensor; and using a computer processing system to: identify a portion ofthe plurality of light intensity values associated with one of the atleast one reflective surface; and analyze the portion of the pluralityof light intensity values; and determine at least one retroreflectivityvalue for the one of the at least one reflective surface using theportion of the plurality of light intensity values, the captured colorimage, and at least one of: a sheeting color of the background surfaceof the at least one reflective surface and a sheeting color of theforeground surface of the at least one reflective surface.
 17. Themethod of claim 16, wherein determining a plurality of light intensityvalues comprises measuring luminance.
 18. The method of claim 16,wherein determining a plurality of light intensity values comprisesmeasuring light intensity.
 19. A method of automated determination ofretroreflectivity values for reflective surfaces disposed along aroadway comprising: activating a light source to emit light on a capturevehicle, while the capture vehicle is moving along the roadway, toilluminate an area that includes a reflective surface, the reflectivesurface including a foreground surface and a background surface;determining a plurality of light intensity values with a light sensordirected to cover a field of view which includes at least a portion ofthe area illuminated by the light source; while the capture vehicle ismoving, capturing a color image with a camera mounted on the capturevehicle and directed to cover a field of view which includes at least aportion of the area illuminated by the light source, the camera beingdistinct from the light sensor; and using a computer processing systemto: identify a portion of the plurality of light intensity values, theportion of the plurality of light intensity values being associated withthe reflective surface; analyze the portion of the plurality of lightintensity values; and determine a retroreflectivity value for thereflective surface using the portion of the plurality of light intensityvalues, the captured color image, and at least one of: a sheeting colorof the background surface of the reflective surface and a sheeting colorof the foreground surface of the reflective surface.
 20. The method ofclaim 19, wherein the light emitted by the light source has at least twowavelengths.
 21. The method of claim 19, wherein the light emitted bythe light source includes full-spectrum visible light.
 22. The method ofclaim 19, wherein the light emitted by the light source includes uniformfull-spectrum visible light.
 23. The method of claim 19, whereindetermining plurality of light intensity values is performed by a camerathat includes the light sensor.
 24. The method of claim 19, whereinusing the computer processing system to determine the retroreflectivityvalue is further based on a position of the light source, a position ofthe light sensor and a position of the reflective surface.
 25. Themethod of claim 24, wherein using the computer processing system todetermine the retroreflectivity value is further based on at least oneof: (i) the position of the light sensor relative to the position of thereflective surface, (ii) the position of the light sensor relative tothe position of the light source and (iii) position of the light sourcerelative to the position of the reflective surface.
 26. The method ofclaim 19, wherein using the computer processing system to determine theretroreflectivity value is further based on a characterization profileof the light source and a characterization profile of the light sensor.27. The method of claim 19, wherein using the computer processing systemto determine the retroreflectivity value is further based on an angulardisplacement between a position of the light source and a position ofthe light sensor relative to a position of the reflective surface. 28.The method of claim 19, wherein using the computer processing system todetermine the retroreflectivity value is further based on an angulardisplacement of a path of the light emitted by the light source on thereflective surface relative to an axis normal to the reflective surface.29. The method of claim 19, wherein activating the light source isperformed without targeting the light source on the reflective surface.30. The method of claim 19, wherein the step activating the light sourceis synchronized to the step of determining the plurality of lightintensity values.
 31. The method of claim 19, wherein the plurality oflight intensity values includes light intensity values representative ofthe background surface of the reflective surface, and whereindetermining the retroreflectivity value for the reflective surfaceincludes using the light intensity values representative of thebackground surface of the reflective surface.
 32. The method of claim31, wherein determining a retroreflectivity value for the reflectivesurface includes determining a luminance of the background surface ofthe reflective surface using the light intensity values representativeof the background surface of the reflective surface.
 33. The method ofclaim 19, wherein the plurality of light intensity values includes lightintensity values representative of the foreground surface of thereflective surface, and wherein determining the retroreflectivity valuefor the reflective surface includes using the light intensity valuesrepresentative of the foreground surface of the reflective surface. 34.The method of claim 33, wherein determining a retroreflectivity valuefor the reflective surface includes determining a luminance of theforeground surface of the reflective surface using the light intensityvalues representative of the foreground surface of the reflectivesurface.
 35. The method of claim 19, wherein determining aretroreflectivity value for the reflective surface includes using acolor characterization profile of the light source.
 36. A system forautomated determination of a retroreflectivity value of a reflectivesurface dispose along a roadway, the system comprising: a control systemoperably coupled to a light source, a camera and a light sensor, thecamera being distinct from the light sensor, the control systemconfigured to: activate the light source to emit light on a capturevehicle, while the capture vehicle is moving along the roadway, toilluminate an area that includes a reflective surface, the reflectivesurface including a foreground surface and a background surface,activate the light sensor to determine a plurality of light intensityvalues directed to cover a field of view which includes at least aportion of the area illuminated by the light source; and while thecapturing vehicle is moving, capture a color image with a camera mountedon the capture vehicle and directed to cover a field of view whichincludes at least a portion of the area illuminated by the light source;a computer processing system configured to: identify a portion of the ofplurality of light intensity values, the portion of the plurality oflight intensity values being associated with the reflective surface;analyze the portion of the plurality of light intensity values; anddetermine a retroreflectivity value for the reflective surface using theportion of the plurality of the light intensity values, the capturedcolor image, and at least one of: a sheeting color of the backgroundsurface of the reflective surface and a sheeting color of the foregroundsurface of the reflective surface.
 37. The system of claim 36, whereinthe light emitted by the light source has at least two wavelengths. 38.The system of claim 36, wherein the light emitted by the light sourceincludes full-spectrum visible light.
 39. The system of claim 36,wherein the light emitted by the light source includes uniformfull-spectrum visible light.
 40. The system of claim 36, whereindetermining a plurality of light intensity values is performed by acamera that includes the light sensor.
 41. The system of claim 36,wherein the computer processing system is further configured todetermine the retroreflectivity value based on a position of the lightsource, a position of the light sensor and a position of the reflectivesurface.
 42. The method of claim 41, wherein the computer processingsystem is further configured to determine the retroreflectivity valuebased on at least one of: (i) the position of the light sensor relativeto the position of the reflective surface, (ii) the position of thelight sensor relative to the light source and (iii) the position of thelight source relative to the position of the reflective surface.
 43. Thesystem of claim 36, wherein the computer processing system is furtherconfigured to determine the retroreflectivity value based on acharacterization profile of the light source and a characterizationprofile of the light sensor.
 44. The system of claim 36, wherein thecomputer processing, system is further configured to determine theretroreflectivity value based on an angular displacement between aposition of the light source and a position of the light sensor relativeto a position of reflective surface.
 45. The system of claim 36, whereinthe computer processing system is further configured to determine theretroreflectivity value based on an angular displacement of a path ofthe light emitted by the light source on the reflective surface relativeto an axis normal to the reflective surface.
 46. The system of claim 36,wherein the control system is configured to activate the light sourcewithout targeting the light source on the reflective surface.
 47. Thesystem of claim 36, wherein the control system is configured tosynchronize activation of the light source and determination of theplurality of light intensity values.
 48. The system of claim 36, whereinthe plurality of light intensity values includes light intensity valuesrepresentative of the background surface of the reflective surface, andwherein the computer processing system is further configured todetermine the retroreflectivity value for the reflective surfaceincludes using the light intensity values representative of thebackground surface of the reflective surface.
 49. The system of claim48, wherein the computer processing system is further configured todetermine a retroreflectivity value for the reflective surface bydetermining a luminance of the background surface of the reflectivesurface using the light intensity values representative of thebackground surface of the reflective surface.
 50. The system of claim36, wherein the plurality of light intensity values includes lightintensity values representative of the foreground surface of thereflective surface, and wherein the computer processing system isfurther configured to determine the retroreflectivity value for thereflective surface using the light intensity values representative ofthe foreground surface of the reflective surface.
 51. The system ofclaim 50, wherein the computer processing system is further configuredto determine a retroreflectivity value for the reflective surface bydetermining a luminance of the foreground surface of the reflectivesurface using the light intensity values representative of theforeground surface of the reflective surface.
 52. The system of claim36, wherein the computer processing system is further configured todetermine a retroreflectivity value for the reflective surface using acolor characterization profile of the light source.